The discussion delves into the mechanics of a feed-forward neural network, highlighting the transformation of a 512-dimensional vector through a series of layers. By increasing and then reducing dimensionality, the model prepares to predict the next word in a sequence. The final stage involves a linear transformation followed by a Softmax function, crucial for mapping outputs to a vocabulary of around 200,000 words.
